Citations

Purpose: The purpose of this study is to characterize the clinical features, management strategies, and outcomes of endophthalmitis cases following pars plana vitrectomy using data from the American Academy of Ophthalmology (Academy) IRIS ® Registry (Intelligent Research in Sight). Methods: This is a retrospective cohort study of eyes that developed endophthalmitis following pars plana vitrectomy between 2013 and 2022. Data was collected from the Academy IRIS Registry. This study analyzed differences in visual acuity, intraocular pressure, rates of evisceration and enucleation, and rates of rhegmatogenous retinal detachment following endophthalmitis diagnosis. Results: A total of 1,223,777 unique eyes underwent a PPV during the study period, with 1,275 eyes meeting the inclusion criteria. The median time from pars plana vitrectomy to endophthalmitis diagnosis was 10 days. The majority of eyes (80.9%) were managed with intravitreal antibiotics alone, without a repeat pars plana vitrectomy. Long-term visual acuity, intraocular pressure, cup-to-disc ratio, and rate of evisceration or enucleation did not differ significantly between treatment groups. However, eyes that underwent the initial pars plana vitrectomy for retinal detachment repair had poorer long-term vision and higher rates of retinal detachment following endophthalmitis. Overall, 16.1% of eyes required evisceration or enucleation in this cohort. Conclusions: Despite its rarity, post-pars plana vitrectomy endophthalmitis can cause significant morbidity and even globe loss. There were no significant differences observed between eyes that were treated with intravitreal tap and inject versus repeat pars plana vitrectomy.

Left atrial (LA) myopathy plays a critical role in the pathophysiology of heart failure with preserved ejection fraction (HFpEF). This study aimed to evaluate LA function in a murine model of hypertension-induced HFpEF, with and without obesity, by analyzing left ventricular (LV) pressure signals to quantify the contribution of LA systole to total LV volume. HFpEF was induced in C57BL/6J mice using the SAUNA model, which involves uninephrectomy, chronic aldosterone infusion, and high-salt drinking water for four weeks. To model obesity-associated HFpEF, a high-fat diet (HFD) was added to the SAUNA protocol. Invasive hemodynamic measurements were obtained, and a novel method was developed to assess atrial systole using the second-order derivative of the LV pressure signal during diastolic filling. SAUNA HFpEF mice exhibited preserved LVEF, moderate hypertension, pulmonary congestion, and diastolic dysfunction as evidenced by a prolonged time constant of relaxation (Tau). In sham control mice, atrial systole accounted for 23.0 ± 2.4% of LV volume, whereas this contribution was significantly reduced to 13.9 ± 2.3% in SAUNA HFpEF mice ( P < 0.01). In obese sham controls, atrial systole contributed 53.1 ± 2.2%, which was markedly decreased to 36.8 ± 3.6% in obese-SAUNA HFpEF mice ( P < 0.003). This novel method enables precise quantification of LA systolic contribution to LV filling in mice and highlights the pathophysiological effects of hypertension and obesity on LA function in HFpEF.

Abstract Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20–30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq®, BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84–1.0) for identifying engraftment fevers with a mean AUROC of 0.88 ± 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning–based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.

Abstract Background aneurysmal subarachnoid hemorrhage (aSAH) is neurological emergency associated with substantial mortality and disability. Current grading systems such as the modified Fisher Scale (mFS) and World Federation of Neurological Societies (WFNS) score, rely on semiquantitative and examination based assessments. Hence, they demonstrate limited predictive precision. The enhanced subarachnoid hemorrhage (eSAH) score is a simplified quantitative model integrating age, Glasgow Coma Scale (GCS), and cisternal subarachnoid hemorrhage volume (SAHV) to predict clinical outcomes after aSAH. Methods We performed a retrospective multicenter cohort study that included 1088 patients across three tertiary-care centers the United States. Predictive performance for unfavorable functional outcome, in-hospital mortality and delayed cerebral ischemia (DCI) was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Comparative analyses were performed and compared to the WFNS and mFS grading systems. Results the eSAH score demonstrated excellent discrimination for unfavorable functional outcome at discharge ( AUC 0.89 ) and in-hospital mortality (AUC 0.87). The DCI subscore demonstrated good discriminatory performance for predicting DCI (AUC 0.77). Compared with conventional grading systems, this was superior to both the WFNS (AUC 0.75) and the mFS ( AUC 0.70). increasing eSAH scores were additionally associated with progressively higher rates of mortality and unfavorable functional outcomes. Conclusion the eSAH score demonstrates strong external validity, reproducibility and superior predictive performance compared with conventional grading systems in a large multicenter cohort. These findings support the clinical utility of quantitative hemorrhage burden integration for early risk stratification in patients with aSAH.

Abstract Objective Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of survivors. Existing prediction tools, including the Chronic Hydrocephalus Ensuing from SAH Score (CHESS), have limited discriminative accuracy. We developed the CHECKMATE score, a clinically practical tool to improve prediction of ventriculoperitoneal shunt dependency after aSAH. Methods In this multicenter retrospective cohort of 486 patients with aSAH from Mayo Clinic (January 1, 2006-December 31, 2021), we used multivariable logistic regression and machine learning to identify independent predictors of ventriculoperitoneal shunt placement. The CHECKMATE score was derived from 5 weighted variables: symptomatic hydrocephalus (10 points), intraventricular hemorrhage (5 points), SAH volume greater than 10 mL (3 points), neutrophil-to-lymphocyte ratio greater than 12 (2 points), and 10-year incremental age thresholds starting at older than 60 years (1 point each). Results Of 486 patients (mean age, 56.3 years; 64.6% female), 137 (28.2%) required ventriculoperitoneal shunt placement. The CHECKMATE score achieved an area under the curve of 0.808 (compared to 0.737 for CHESS), with a sensitivity of 0.85, specificity of 0.67, and negative predictive value of 0.92 at the optimal cutoff of 14 points. Conclusions The CHECKMATE score outperforms CHESS for predicting ventriculoperitoneal shunt dependency after aSAH and is easily used at the bedside. Its high negative predictive value helps identify low-risk patients who may benefit from earlier external ventricular drain weaning and shorter hospital stays.

Abstract Grey matter network topology is altered in Alzheimer’s disease and these alterations are related to cognitive decline. Understanding the biological underpinnings of loss of brain connectivity may provide insights into mechanisms related to developing Alzheimer’s dementia (i.e. dementia A+). We investigated which biological processes as measured in CSF proteomics were associated with loss of brain connections across the Alzheimer’s disease continuum. We included 347 individuals with abnormal CSF amyloid [mean age ± standard deviation (SD) 66 ± 8; 98 cognitively unimpaired—A+, 88 mild cognitive impairment—A+, 161 dementia A+] and 146 cognitively unimpaired individuals with normal CSF amyloid (mean age ± SD 62 ± 8) and available T1w MRI-scans and CSF proteomic data (3097 proteins using tandem mass tag spectrometry) from the Amsterdam Dementia Cohort. We used an automated pipeline to construct grey matter networks from 3D-T1 sequences and for each network, calculated the small-worldness coefficient, which we previously found to be robustly related to cognitive decline. Linear models were applied to test associations between CSF protein levels and connectivity measures using an interaction term for clinical stage while controlling for connectivity density, age and sex. We validated our results in data from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Pathway enrichment analysis was performed for proteins associated with loss of brain connectivity (P < 0.05) using the Gene Ontology database. Individuals across the Alzheimer’s disease continuum had lower small-worldness coefficients compared with controls (ANOVA P < 0.001). In amyloid positive individuals, higher levels of 222 proteins and lower levels of 482 proteins were associated with lower small-worldness coefficients and were enriched for innate immune system and neuroplasticity pathways, respectively. Stratified for disease stage, most protein associations with lower small-worldness coefficients were found in mild cognitive impairment A+ (n = 527 proteins) and dementia A+ (n = 799 proteins) with considerable overlap (n = 239 proteins). Proteins in these stages were enriched for complement activation and synaptic integrity. In cognitive unimpairment A+, we found proteins enriched for processes involved in apoptosis. We did not find any enriched biological processes in controls. Repeating analyses in ADNI indicated that similar biological processes were associated with altered grey matter network connectivity. Higher CSF levels of proteins involved in immune responses and lower levels of proteins related to neuroplasticity were associated with lower small-worldness coefficients across the Alzheimer’s disease continuum. This suggests that preserving cognitive function in the presence of amyloid and prevention of dementia A+ may require therapies that strengthen synapses and targets the innate immune system in addition to amyloid and tau.

TPS655 Background: Early (14-28 day) on-treatment suppression of tumor proliferation during neoadjuvant endocrine therapy (NET) is strongly associated with favorable long-term outcomes in estrogen receptor–positive (ER+) HER2– breast cancer. In premenopausal women, tamoxifen with or without ovarian function suppression (OFS) results in suboptimal Ki-67 suppression compared with aromatase inhibitor + OFS. (Z)-endoxifen (ENDX), the active metabolite of tamoxifen, dually inhibits ER signaling and PKCβ1-mediated AKT activation, supporting its evaluation as an alternative NET strategy in this population. Methods: EVANGELINE (NCT05607004) is an ongoing, multicenter, open-label Phase 2 study evaluating daily 40 mg ENDX plus goserelin administered every 28 days as neoadjuvant therapy in premenopausal women with ER+/HER2–, cT2–3, cN0–1 breast cancer. The primary objective is to determine the proportion of patients with baseline Ki-67 >10% who achieve Ki-67 ≤10% after 4 weeks of therapy. A Simon two-stage design is used to test whether the Week 4 Ki-67 ≤10% rate is at least 65%, with 20 patients enrolled into the first stage and, if promising, another 25 patients enrolled into the second stage (cohort A). A parallel cohort of 20 patients with baseline Ki-67 ≤10% (cohort B) is enrolled to assess objective response rate (ORR) at 24 weeks per RECIST v1.1. Secondary objectives include examining safety and tolerability, residual cancer burden, and PEPI score. Correlative analyses include examining effect of treatment on select tumor and plasma biomarkers. Clinical trial information: NCT05607004 .

e13677 Background: The development of AI led to the emergence of tools such as AIS, which have been increasingly adopted in medicine. However, formal evaluation of barriers and clinical care gaps in HemOnc is limited, particularly regarding treatment plan complexities and toxicity documentation. Methods: Structured investigator-developed surveys about AIS availability, adoption, benefits, and challenges were developed on Qualtrics and approved by the IRB. HemOnc faculty physicians (FP), advanced practice providers (APP), and trainees (HOT) were surveyed between 11/14/25-1/16/26. Surveys were distributed in the USA via email, social media (on X), and ASCO myConnection. Descriptive statistics were used for analysis. Results: 132 participants responded to the survey: 55 FP (42%), 14 APP (11%), and 63 HOT (48%). 80% of them reported having AIS at their institutions, and 54% of those reported using these tools in their clinical practice. Among FP and APP, 72% practiced in academic settings, 14% in community, and 13% in hybrid centers. Among those not using AIS, identified barriers were issues with note formatting (56%), lack of training (25%), and concerns that the note content is incorrect or inadequate (25%). 60% are willing to use AIS if note formatting matched their preference, and 42% would try it if contents better reflected the patient encounter. Table 1 details the proportion of encounters using AIS, the portion of documentation it was used for, and the perceived challenges with these tools. Among AIS users, the average satisfaction score was 5.3 on a 7 point scale, corresponding to participants being somewhat satisfied. Participants agreed slightly that AIS had reduced their feeling of work-related burnout, with an average score of 3.6 on a 5 point scale. Overall, participants felt neutral about how well-suited AIS are for HemOnc, with an average score of 4.0 on a 7 point scale. Conclusions: Despite AIS being widely available, 46% of the surveyed HemOnc workforce do not use them in clinical practice. Those who do reported decreased time spent on documentation outside work hours and a slight reduction in the feeling of burnout. Formatting issues and record integration remain barriers, with substantial concerns about AIS not adequately describing HemOnc treatment plans. To bridge the last mile of integration into HemOnc, partnership between AIS developers and the clinical workforce is essential to meet clinical needs, while prioritizing patient safety and data privacy. FP + APP % HOT % Patient encounters where AIS is used 62 44 Used AIS for the subjective portion of note 68 96 Used AIS for the assessment/plan portions of note 40 25 AIS decreased the time on clinical documentation outside work hours 96 93 AIS note formatting needs improvement 68 57 Issues integrating AIS and outside records 46 71 AIS note contents about the plan are suboptimal 32 61

Background Tacrolimus dosing after kidney transplantation is complex, highly individualized, and prone to variability, which can impact graft outcomes. While machine learning (ML) approaches have been used primarily to predict tacrolimus concentrations, large language models (LLMs) may enable protocol-constrained clinical decision support by generating dosing recommendations aligned with established treatment guidelines. Methods We developed TacroDose AI, a protocol-constrained LLM based on GPT-4, to generate tacrolimus dosing recommendations aligned with institutional guidelines. Using 300 structured simulated clinical scenarios, we evaluated protocol adherence, error patterns, and reproducibility of model-generated dosing recommendations. Following review of initial outputs, a refined prompt ( TacroAI 2.0 ) was implemented to strengthen protocol constraints and structured output verification, and performance was reassessed. Results TacroDose AI achieved 72.9% protocol adherence, with 27.1% protocol deviations, including 13.9% sentinel errors with potential clinical significance. Prompt refinement substantially improved performance: TacroAI 2.0 achieved 91.7% adherence, reduced sentinel errors to 1.0%, and improved reproducibility from 77.3 to 86.7% (all p < 0.01). Although rounding discrepancies increased, these represented minor numerical differences without clinical relevance. Performance improvements were observed across subtherapeutic, therapeutic, and supratherapeutic tacrolimus scenarios. Conclusion Protocol-constrained LLMs can produce guideline-concordant tacrolimus dosing recommendations with high adherence and improved reliability in simulated clinical settings. These findings highlight a potential framework for integrating generative AI with rule-based clinical protocols to support safe medication management. With further validation using real-world data, such systems could be integrated into electronic health records to support clinician-supervised, protocol-based immunosuppressive dosing in transplant care.

Abstract Somatic mutations that arise post-zygotically create genetic diversity among normal human cells and provide key insights into human development and aging. Fibroblast-derived induced pluripotent stem cells (iPSCs) have proved to be a useful system for disease modelling; however, due to their clonal nature, iPSC lines carry somatic mutations inherited from the founder cells, raising concerns about their genomic integrity. At the same time, this clonality enables single-cell–level discovery of somatic mutations and the reconstruction of developmental lineages. In living individuals, though, this approach requires invasive biopsies and is limited to skin-derived lineages. Here, we generated 33 urine-derived iPSC lines from four males representing two father– son relationships, performed shallow whole-genome sequencing of the lines and analyzed somatic mutations. Derived iPSCs representing single cells from urine carried a few hundred of somatic single-nucleotide variants per genome, dominated by endogenous, clock-like mutational signatures and lacking environmental imprints such as UV-associated mutations. Copy-number analysis identified somatic CNVs in most of the lines and revealed higher CNV burdens in fathers than in sons, consistent with age-related structural mosaicism. Shared mutations across lines enabled reconstruction of cell lineage phylogenetic trees. In summary, urine-derived iPSCs showed genomic alterations comparable to those in fibroblast-derived iPSC lines and represent a valuable non-invasive alternative for disease modeling. Overall, this study provides the first genome-wide characterization of somatic mutations in urine-derived iPSCs and establishes them as a practical and non-invasive platform for charting somatic mutation landscapes and tracing developmental lineages in living humans.